Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads

Nature Methods volume 18, pages 1322–1332 (2021)Cite this article

Subjects

Abstract

Long-read sequencing has the potential to transform variant detection by reaching currently difficult-to-map regions and routinely linking together adjacent variations to enable read-based phasing. Third-generation nanopore sequence data have demonstrated a long read length, but current interpretation methods for their novel pore-based signal have unique error profiles, making accurate analysis challenging. Here, we introduce a haplotype-aware variant calling pipeline, PEPPER-Margin-DeepVariant, that produces state-of-the-art variant calling results with nanopore data. We show that our nanopore-based method outperforms the short-read-based single-nucleotide-variant identification method at the whole-genome scale and produces high-quality single-nucleotide variants in segmental duplications and low-mappability regions where short-read-based genotyping fails. We show that our pipeline can provide highly contiguous phase blocks across the genome with nanopore reads, contiguously spanning between 85% and 92% of annotated genes across six samples. We also extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance over the current WhatsHap-DeepVariant standard. Finally, we demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy (Q35+ nanopore-polished and Q40+ PacBio HiFi-polished).

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Nanopore variant-calling results.
Fig. 2: Comparison between Nanopore, Illumina and PacBio HiFi variant calling performance.
Fig. 3: Margin and WhatsHap phasing results.
Fig. 4: Gene analysis.
Fig. 5: Diploid assembly-polishing results.

Similar content being viewed by others

Data availability

We have made the analysis data available publicly (variant calling outputs, genome assemblies) in: https://console.cloud.google.com/storage/browser/pepper-deepvariant-public/analysis_data. The source data for the main figures can be found in: https://console.cloud.google.com/storage/browser/pepper-deepvariant-public/figure_source_data/Figure_source_data/.

For sequencing data, we used several publicly available datasets:

• GIAB consortium26,28: https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/

• Human Pangenome Reference Consortium (HPRC): https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html

• Telomere-to-telomere consortium11,12: https://github.com/nanopore-wgs-consortium/CHM13

Please see the Supplementary Notes to find specific links to the sequencing data that we used for our analysis. Source data are provided with this paper.

Code availability

The modules of PEPPER-Margin-DeepVariant are publicly available in these repositories:

• PEPPER: https://github.com/kishwarshafin/pepper

• Margin: https://github.com/UCSC-nanopore-cgl/margin

• DeepVariant: https://github.com/google/deepvariant

The PEPPER-Margin-DeepVariant software57 is available at https://doi.org/10.5281/zenodo.5275510, and we used r0.4 version for the evaluation presented in this manuscript. For simpler use, we have also created a publicly available docker container, kishwars/pepper_deepvariant:r0.4, that can run our variant-calling and polishing pipelines.

References

  1. Altshuler, D. M. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  CAS  Google Scholar 

  2. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Li, W. & Freudenberg, J. Mappability and read length. Front. Genet. 5, 381 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chaisson, M. J. P. et al. Multi-platform discovery of haplotype-resolved structural variation in human genomes. Nat. Commun. 10, 1784 (2019).

  5. Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Falconer, E. & Lansdorp, P. M. Strand-seq: a unifying tool for studies of chromosome segregation. Semin. Cell Developmental Biol. 24, 643–652 (2013).

    Article  CAS  Google Scholar 

  7. Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Jain, M. et al. Improved data analysis for the MinION nanopore sequencer. Nat. Methods 12, 351 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. Jain, C., Rhie, A., Hansen, N., Koren, S. & Phillippy, A. M. A long read mapping method for highly repetitive reference sequences. Preprint at https://doi.org/10.1101/2020.11.01.363887 (2020).

  11. Miga, K. H. et al. Telomere-to-telomere assembly of a complete human X chromosome. Nature 585, 79–84 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Logsdon, G. A. et al. The structure, function and evolution of a complete human chromosome 8. Nature 593, 7857 (2021).

  13. Shafin, K. et al. Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes. Nat. Biotechnol. 38, 1044–1053 (2020).

  14. Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods 18, 170–175 (2021).

  15. Nurk, S. et al. HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads. Genome Res. 30, 1291–1305 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. nanoporetech/medaka: sequence correction provided by ONT Research, https://github.com/nanoporetech/medaka (Oxford Nanopore Technologies, 2018).

  19. Luo, R. et al. Exploring the limit of using a deep neural network on pileup data for germline variant calling. Nat. Mach. Intell. 2, 220–227 (2020).

    Article  Google Scholar 

  20. Edge, P. & Bansal, V. Longshot enables accurate variant calling in diploid genomes from single-molecule long read sequencing. Nat. Commun. 10, 1–10 (2019).

    Article  CAS  Google Scholar 

  21. Wenger, A. M. et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat. Biotechnol. 37, 1155–1162 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ebler, J., Haukness, M., Pesout, T., Marschall, T. & Paten, B. Haplotype-aware diplotyping from noisy long reads. Genome Biol. 20, 116 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Huddleston, J. et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 27, 677–685 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sedlazeck, F. J. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods 15, 461–468 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Patterson, M. D. et al. WhatsHap: weighted haplotype assembly for future-generation sequencing reads. J. Comput. Biol. 22, 498–509 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3, 160025 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wagner, J. et al. Benchmarking challenging small variants with linked and long reads. Preprint at https://doi.org/10.1101/2020.07.24.212712 (2020).

  28. Olson, N. D. et al. precisionFDA Truth Challenge V2: calling variants from short-and long-reads in difficult-to-map regions. Preprint at https://doi.org/10.1101/2020.11.13.380741 (2020).

  29. Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol. 36, 338 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jain, M. et al. Linear assembly of a human centromere on the Y chromosome. Nat. Biotechnol. 36, 321 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Fiddes, I. T. et al. Comparative Annotation Toolkit (CAT)—simultaneous clade and personal genome annotation. Genome Res. 28, 1029–1038 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Eichler, E. E., Clark, R. A. & She, X. An assessment of the sequence gaps: unfinished business in a finished human genome. Nat. Rev. Genet. 5, 345 (2004).

    Article  CAS  PubMed  Google Scholar 

  33. Euskirchen, P. et al. Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing. Acta Neuropathol. 134, 691–703 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rang, F. J., Kloosterman, W. P. & de Ridder, J. From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy. Genome Biol. 19, 90 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Chin, C.-S. et al. A diploid assembly-based benchmark for variants in the major histocompatibility complex. Nat. Commun. 11, 1–9 (2020).

    Article  CAS  Google Scholar 

  36. Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. 27, 801–812 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rodriguez, O. L. et al. A novel framework for characterizing genomic haplotype diversity in the human immunoglobulin heavy chain locus. Front. Immunol. 11, 2136 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chin, C.-S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Koren, S. et al. De novo assembly of haplotype-resolved genomes with trio binning. Nat. Biotechnol. 36, 1174 (2018).

    Article  CAS  Google Scholar 

  41. Porubsky, D. et al. Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads. Nat. Biotechnol. 39, 302–308 (2021).

    Article  CAS  PubMed  Google Scholar 

  42. Harrow, J. et al. GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zook, J. M. et al. An open resource for accurately benchmarking small variant and reference calls. Nat. Biotechnol. 37, 561 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Baid, G. et al. An extensive sequence dataset of gold-standard samples for benchmarking and development. Preprint at https://doi.org/10.1101/2020.12.11.422022 (2020).

  45. Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

    Article  CAS  PubMed  Google Scholar 

  46. Heller, D. & Vingron, M. SVIM-asm: Structural variant detection from haploid and diploid genome assemblies. Bioinformatics 36, 22–23 (2020).

    Google Scholar 

  47. Zook, J. M. et al. A robust benchmark for detection of germline large deletions and insertions. Nat. Biotechnol. 38, 1347–1355 (2020).

  48. Tewhey, R., Bansal, V., Torkamani, A., Topol, E. J. & Schork, N. J. The importance of phase information for human genomics. Nat. Rev. Genet. 12, 215–223 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Browning, S. R. & Browning, B. L. Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12, 703–714 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Glusman, G., Cox, H. C. & Roach, J. C. Whole-genome haplotyping approaches and genomic medicine. Genome Med. 6, 1–16 (2014).

    Article  CAS  Google Scholar 

  51. Li, H. et al. A synthetic-diploid benchmark for accurate variant-calling evaluation. Nat. Methods 15, 595–597 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Krusche, P. et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat. Biotechnol. 37, 555–560 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Cleary, J. G. et al. Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data. J. Comput. Biol. 21, 405–419 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. Newey, W. K. Adaptive estimation of regression models via moment restrictions. J. Econom. 38, 301–339 (1988).

    Article  Google Scholar 

  57. K. Shafin, et al. PEPPER-Margin-DeepVariant (version r0.4), https://doi.org/10.5281/zenodo.5275510 (Zenodo, 2021).

Download references

Acknowledgements

Research reported in this publication was supported by the National Human Genome Research Institute of the National Institutes of Health under award numbers U41HG010972, R01HG010485, U01HG010961, and OT2OD026682 (K.S., T.P., M.K., J.M.E, K.H.M., M.J., B.P.). We thank Circulomics Inc. for sharing HG001 Nanopore data. We thank J. Zook and J. Wagner from the National Institute of Standards and Technology (NIST) for providing a draft version of the HG005 v4.2.1 benchmark. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. These authors contributed equally: Kishwar Shafin, Trevor Pesout, Pi-Chuan Chang.

Authors and Affiliations

  1. UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA

    Kishwar Shafin, Trevor Pesout, Mikhail Kolmogorov, Jordan M. Eizenga, Karen H. Miga, Miten Jain & Benedict Paten

  2. Google Inc, Mountain View, CA, USA

    Pi-Chuan Chang, Maria Nattestad, Alexey Kolesnikov, Sidharth Goel, Gunjan Baid & Andrew Carroll

  3. Chan Zuckerberg Initiative, Redwood City, CA, USA

    Paolo Carnevali

Authors
  1. Kishwar Shafin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Trevor Pesout
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pi-Chuan Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria Nattestad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexey Kolesnikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sidharth Goel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gunjan Baid
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mikhail Kolmogorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jordan M. Eizenga
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Karen H. Miga
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Paolo Carnevali
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Miten Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Andrew Carroll
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Benedict Paten
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.P. and A.C. designed and executed the study. K.S. developed PEPPER. T.P. developed Margin. P.-C.C. designed candidate import functionality in DeepVariant. K.S., T.P., and P.-P.C. contributed equally to the methods development and core analysis presented. M.N. designed alt-event alignment in DeepVariant, A.K. contributed to haplotype sorting and improvements on DeepVariant runtime, S. G. contributed to candidate import module of DeepVariant, G.B. designed and executed the post-processing model to improve multiallelic variant accuracy. M.K. designed and evaluated assembly polishing. J.M.E. designed local phasing metric and contributed to phasing evaluation. K.H.M. provided experimental design guidance, and P.C. generated assemblies and provided guidance on assembly polishing. M.J. performed nanopore sequencing, quality control and helped to design and execute analysis. All authors approve of the final manuscript.

Corresponding authors

Correspondence to Andrew Carroll or Benedict Paten.

Ethics declarations

Competing interests

K.S. has performed paid internships at NVIDIA Corp and Google. P.C., M.N., A.K., S.G., G.B., and A.C. are employees of Google and own Alphabet stock as part of the standard compensation package. M.J. has received reimbursement for travel, accommodation, and conference fees to speak at events organized by ONT. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Ruibang Luo and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes, Supplementary Figures 1–10, and Supplementary Tables 1–31

Reporting Summary

Peer Review Information

Source data

Source Data Fig. 1

Statistical source data

Source Data Fig. 2

Statistical source data

Source Data Fig. 3

Statistical source data

Source Data Fig. 4

Statistical source data

Source Data Fig. 5

Statistical source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shafin, K., Pesout, T., Chang, PC. et al. Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads. Nat Methods 18, 1322–1332 (2021). https://doi.org/10.1038/s41592-021-01299-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-021-01299-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research